Click here for interactive plots:

https://kforthman.shinyapps.io/COVID19_Interactive_Plots/

Click here to see how cities and counties overlap:

https://kforthman.shinyapps.io/500citiescounties

Compare COVID stats to Neighborhood Factors

data.cor <- cor(county.Demo_and_Covid.allcounties[,-1], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

Compare COVID stats to 500 cities data and Neighborhood Factors

data.cor2 <- cor(county.Demo_and_Covid.500counties[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

corrplot.mixed(data.cor2[7:13,c(1:5, 14:42,6)], upper = 'ellipse', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

—-Linear Mixed Effects Model —-

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = county.Demo_and_Covid.500counties)
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -1204.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0543 -0.3068 -0.0716  0.1949  6.2956 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000001883 0.001372
##  Residual             0.000012744 0.003570
## Number of obs: 178, groups:  stateID, 33
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0122143040   0.0097856419  76.1043940014
## Affluence                    0.0047067502   0.0011081056 112.1024549167
## Singletons.in.Tract          0.0007816757   0.0008973067 148.1007623173
## Seniors.in.Tract             0.0005433391   0.0011808434 154.5346227933
## African.Americans.in.Tract   0.0009970831   0.0009862300 155.3576126389
## Noncitizens.in.Tract         0.0009621312   0.0007643654 129.1214899898
## High.BP                      0.0001896513   0.0001876928 119.8244080119
## Binge.Drinking               0.0001757468   0.0001606959  49.0494202896
## Cancer                      -0.0012147384   0.0011060181 112.0213097301
## Asthma                       0.0008260257   0.0005721362  53.9918785984
## Heart.Disease                0.0016751064   0.0013132952  84.5149564622
## COPD                        -0.0003199412   0.0010931237  83.8551038978
## Smoking                     -0.0000256067   0.0002278597  89.9936942637
## Diabetes                    -0.0006666581   0.0005367303  89.0741347081
## No.Physical.Activity        -0.0000691120   0.0002066629  98.2361195842
## Obesity                      0.0002817590   0.0001767625 119.3793978661
## Poor.Sleeping.Habits        -0.0000553327   0.0001645576 130.5553831619
## Poor.Mental.Health          -0.0000865825   0.0004335174  34.9627004371
## Testing_Rate                 0.0000006762   0.0000002810  44.8879829548
## Hospitalization_Rate        -0.0000575238   0.0000937872  31.3583542101
##                            t value  Pr(>|t|)    
## (Intercept)                 -1.248    0.2158    
## Affluence                    4.248 0.0000448 ***
## Singletons.in.Tract          0.871    0.3851    
## Seniors.in.Tract             0.460    0.6461    
## African.Americans.in.Tract   1.011    0.3136    
## Noncitizens.in.Tract         1.259    0.2104    
## High.BP                      1.010    0.3143    
## Binge.Drinking               1.094    0.2794    
## Cancer                      -1.098    0.2744    
## Asthma                       1.444    0.1546    
## Heart.Disease                1.275    0.2056    
## COPD                        -0.293    0.7705    
## Smoking                     -0.112    0.9108    
## Diabetes                    -1.242    0.2175    
## No.Physical.Activity        -0.334    0.7388    
## Obesity                      1.594    0.1136    
## Poor.Sleeping.Habits        -0.336    0.7372    
## Poor.Mental.Health          -0.200    0.8429    
## Testing_Rate                 2.406    0.0203 *  
## Hospitalization_Rate        -0.613    0.5441    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.105                                                        
## Sngltns.n.T  0.030  0.070                                                 
## Snrs.n.Trct  0.546  0.385  0.196                                          
## Afrcn.Am..T  0.146  0.156 -0.401  0.147                                   
## Nnctzns.n.T -0.007  0.100  0.036  0.063 -0.085                            
## High.BP     -0.023  0.244  0.056  0.106 -0.087  0.388                     
## Bing.Drnkng -0.307 -0.174 -0.293 -0.169  0.072  0.027  0.124              
## Cancer      -0.591 -0.183  0.179 -0.317 -0.071 -0.132 -0.361 -0.090       
## Asthma      -0.403 -0.197 -0.254 -0.214  0.086  0.094  0.168  0.005  0.071
## Heart.Dises -0.155  0.080 -0.300 -0.156  0.249 -0.106 -0.002  0.057 -0.469
## COPD         0.576  0.025  0.155  0.280 -0.022  0.275  0.154  0.086 -0.280
## Smoking     -0.142  0.146 -0.174 -0.103 -0.049  0.015 -0.061 -0.302  0.076
## Diabetes     0.101 -0.350 -0.102 -0.217 -0.305 -0.310 -0.534  0.049  0.232
## N.Physcl.Ac -0.199 -0.033  0.079 -0.028 -0.032 -0.226 -0.087  0.120  0.473
## Obesity      0.004  0.414  0.434  0.302  0.135  0.188 -0.093 -0.228  0.104
## Pr.Slpng.Hb -0.444 -0.389  0.137 -0.354 -0.339 -0.032 -0.187  0.099  0.138
## Pr.Mntl.Hlt -0.352  0.268 -0.067 -0.048  0.098 -0.163 -0.052  0.088  0.330
## Testing_Rat  0.247 -0.085  0.014  0.039  0.021 -0.054 -0.039 -0.030 -0.214
## Hsptlztn_Rt -0.119 -0.237 -0.097 -0.227 -0.062 -0.078 -0.108 -0.131  0.021
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.279                                                        
## COPD        -0.391 -0.563                                                 
## Smoking      0.081  0.207 -0.499                                          
## Diabetes    -0.129 -0.304 -0.075  0.224                                   
## N.Physcl.Ac  0.025 -0.375 -0.018 -0.329 -0.084                            
## Obesity     -0.266 -0.092  0.162 -0.198 -0.382 -0.062                     
## Pr.Slpng.Hb  0.078  0.248 -0.193 -0.029 -0.022 -0.102 -0.166              
## Pr.Mntl.Hlt -0.220  0.087 -0.456  0.067  0.009  0.059  0.077 -0.166       
## Testing_Rat -0.359 -0.040  0.224  0.144  0.130 -0.309  0.123 -0.151 -0.158
## Hsptlztn_Rt  0.096  0.103 -0.104  0.093  0.065 -0.049 -0.028 -0.013 -0.104
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.190
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)", data = county.Demo_and_Covid.500counties)

print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -2469.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6574 -0.3658 -0.0699  0.2437  6.8909 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000007101 0.002665
##  Residual             0.000011517 0.003394
## Number of obs: 326, groups:  stateID, 51
## 
## Fixed effects:
##                                Estimate   Std. Error           df t value
## (Intercept)                 -0.02128652   0.00764922 194.73091984  -2.783
## Affluence                    0.00278248   0.00069323 302.79470992   4.014
## Singletons.in.Tract          0.00083404   0.00064675 300.78502968   1.290
## Seniors.in.Tract             0.00040138   0.00081700 304.40203255   0.491
## African.Americans.in.Tract   0.00163376   0.00078982 306.69369543   2.069
## Noncitizens.in.Tract         0.00167676   0.00063795 273.23086687   2.628
## High.BP                     -0.00001987   0.00014306 299.51338361  -0.139
## Binge.Drinking               0.00037147   0.00015067 161.65234891   2.465
## Cancer                      -0.00032625   0.00083964 268.01984750  -0.389
## Asthma                       0.00064276   0.00049958 143.58592002   1.287
## Heart.Disease                0.00295350   0.00107807 213.93927257   2.740
## COPD                        -0.00117572   0.00081617 208.22445052  -1.441
## Smoking                     -0.00022429   0.00018855 253.53405078  -1.190
## Diabetes                    -0.00109435   0.00040396 270.81616864  -2.709
## No.Physical.Activity         0.00029795   0.00016233 240.06491995   1.835
## Obesity                      0.00022427   0.00013125 307.92946098   1.709
## Poor.Sleeping.Habits         0.00025333   0.00012644 297.82938348   2.003
## Poor.Mental.Health          -0.00013789   0.00042424 105.02124871  -0.325
##                             Pr(>|t|)    
## (Intercept)                  0.00592 ** 
## Affluence                  0.0000754 ***
## Singletons.in.Tract          0.19818    
## Seniors.in.Tract             0.62358    
## African.Americans.in.Tract   0.03943 *  
## Noncitizens.in.Tract         0.00906 ** 
## High.BP                      0.88965    
## Binge.Drinking               0.01473 *  
## Cancer                       0.69791    
## Asthma                       0.20030    
## Heart.Disease                0.00667 ** 
## COPD                         0.15122    
## Smoking                      0.23532    
## Diabetes                     0.00718 ** 
## No.Physical.Activity         0.06768 .  
## Obesity                      0.08850 .  
## Poor.Sleeping.Habits         0.04603 *  
## Poor.Mental.Health           0.74580    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence   -0.052                                                        
## Sngltns.n.T -0.055  0.043                                                 
## Snrs.n.Trct  0.393  0.293  0.073                                          
## Afrcn.Am..T  0.241  0.076 -0.404  0.202                                   
## Nnctzns.n.T -0.072  0.153  0.125  0.058 -0.191                            
## High.BP     -0.094  0.158  0.098  0.008 -0.232  0.326                     
## Bing.Drnkng -0.490 -0.038 -0.205 -0.067  0.041 -0.076  0.148              
## Cancer      -0.494 -0.095  0.231 -0.171 -0.074 -0.066 -0.330 -0.018       
## Asthma      -0.270 -0.095 -0.262 -0.122 -0.015  0.212  0.051  0.009 -0.157
## Heart.Dises -0.059  0.078 -0.301 -0.132  0.213 -0.055  0.001  0.034 -0.603
## COPD         0.479  0.008  0.129  0.171 -0.006  0.156  0.057  0.059 -0.212
## Smoking     -0.042  0.105 -0.119 -0.138 -0.104  0.159 -0.082 -0.327  0.156
## Diabetes     0.036 -0.302 -0.078 -0.132 -0.230 -0.251 -0.447  0.074  0.369
## N.Physcl.Ac -0.116  0.035  0.102  0.079  0.059 -0.275  0.004  0.127  0.335
## Obesity     -0.066  0.382  0.398  0.201  0.133  0.193 -0.103 -0.146  0.118
## Pr.Slpng.Hb -0.385 -0.350  0.162 -0.325 -0.321 -0.046 -0.156  0.087  0.028
## Pr.Mntl.Hlt -0.353  0.184 -0.008  0.024  0.052 -0.164  0.029  0.130  0.416
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H
## Affluence                                                          
## Sngltns.n.T                                                        
## Snrs.n.Trct                                                        
## Afrcn.Am..T                                                        
## Nnctzns.n.T                                                        
## High.BP                                                            
## Bing.Drnkng                                                        
## Cancer                                                             
## Asthma                                                             
## Heart.Dises  0.335                                                 
## COPD        -0.321 -0.492                                          
## Smoking      0.144  0.084 -0.475                                   
## Diabetes    -0.106 -0.434 -0.006  0.277                            
## N.Physcl.Ac -0.022 -0.359  0.088 -0.274 -0.168                     
## Obesity     -0.125 -0.021  0.091 -0.220 -0.375 -0.044              
## Pr.Slpng.Hb  0.000  0.239 -0.092 -0.169 -0.061 -0.153 -0.115       
## Pr.Mntl.Hlt -0.438 -0.065 -0.390 -0.029  0.071 -0.087  0.024 -0.080

Testing Rate

testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

Pink highlights the last 14 days.

day.first.case <- min(which(US.total$cases.total > 100))
n.days <- nrow(US.total)

twoweek.col <- c(rep("grey", n.days-day.first.case-13), rep("pink", 14))

par(mar = c(5,5,4,2))
barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$rise.cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Cases of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$rise.deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Deaths of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)